Modeling and analyzing army air assault operations via simulation

It is very important to use combat simulation in personnel training and preparing them for different war scenarios. Simulation modeling and analysis methodologies gives an opportunity to staff officers and commanders to measure the effectiveness of their plans and take necessary precautions. In a simulated environment, different combat scenarios can be tried without actually deploying the units to the combat area and getting 'losts, costs, and risks'. As one of the most complicated and decisive operations on the road to victory, 'air assault operations' are high-risk, high-payoff operations that, when properly planned and vigorously executed, allow commanders to take the initiative in combat areas. In this study, we develop a simulation system called the Air Assault Operations Simulation Model (AAOSM) that allows planners to: (1) analyze air assault operations early in the decision process and refine those models as their decision process evolves, (2) perform 'bottleneck analysis' of the preplanned operations, and (3) perform 'risk management' of the operation before conducting the real operation. AAOSM is developed by using the ARENA simulation programming language. The outputs of the model are analyzed using statistical methods. The factors that have significant effect on air assault operations are identified. The possible scenarios are also evaluated for different weather and terrain conditions and for various refueling and maintenance configurations. © 2011 Simulation Councils Inc

Novel methodology to discern predictors of remission and patterns of disease activity over time using rheumatoid arthritis clinical trials data.

OBJECTIVES: To identify predictors of remission and disease activity patterns in patients with rheumatoid arthritis (RA) using individual participant data (IPD) from clinical trials. METHODS: Phase II and III clinical trials completed between 2002 and 2012 were identified by systematic literature review and contact with UK market authorisation holders. Anonymised baseline and follow-up IPD from non-biological arms were amalgamated. Multiple imputation was used to handle missing outcome and covariate information. Random effects logistic regression was used to identify predictors of remission, measured by the Disease Activity Score 28 (DAS28) at 6 months. Novel latent class mixed models characterised DAS28 over time. RESULTS: IPD of 3290 participants from 18 trials were included. Of these participants, 92% received methotrexate (MTX). Remission rates were estimated at 8.4%(95%CI 7.4%to9.5%) overall, 17%(95%CI 14.8%to19.4%) for MTX-naïve patients with early RA and 3.2% (95% CI 2.4% to 4.3%) for those with prior MTX exposure at entry. In prior MTX-exposed patients, lower baseline DAS28 and MTX reinitiation were associated with remission. In MTX-naïve patients, being young, white, male, with better functional and mental health, lower baseline DAS28 and receiving concomitant glucocorticoids were associated with remission. Three DAS28 trajectory subpopulations were identified in MTX-naïve and MTX-exposed patients. A number of variables were associated with subpopulation membership and DAS28 levels within subpopulations. CONCLUSIONS: Predictors of remission differed between MTX-naïve and prior MTX-exposed patients at entry. Latent class mixed models supported differential non-biological therapy response, with three distinct trajectories observed in both MTX-naïve and MTX-exposed patients. Findings should be useful when designing future RA trials and interpreting results of biomarker studies.This study was funded by the MRC/ABPI Inflammation and Immunology Initiative Grant (MRC reference numbers: G1001516 and G1001518). Dr Brian Tom is supported by the UK Medical Research Council (Unit Programme number MC_UP_1302/3 and MC_UU_00002/2). Deborah Symmons was an NIHR Senior Investigator

Scientific Business Abstracts of the 112th Annual Meeting of the Association of Physicians of Great Britain and Ireland

No abstract available